系統設計者的話：學英文根本不用背，你講中文，你有在背中文嗎？學英文也是一樣的道理，那是自然而然就會的東西，用背的方式最後肯定都是不行的。 更多...

最好的學習就是有英文的父母，不斷的跟你說話、耐心的教導你，時間長了，你也就慢慢就融入其中了。

但是我們沒有英文父母怎麼辦？看影片學英文就學的起來嗎？學不起來。

我們從小到大，也看過了不少英文電影，至少也看過1、200部也跑不掉，但是為什麼看了這麼多的英文電影，英文還是很爛呢？

主要的原因是我們沒有從基礎性的強化學習。舉個例子，西方的幼兒出生時，他至少聽了各種大人講英文至少聽了2年，才慢慢開始說英文、與大人互動。

而且都是一字一句慢慢的說、慢慢的教、漸進式的、慢慢的、越來越習慣、越來越聽的懂、越來越會說的過程。

那麼我們看的影片、電影過程中，都是大人成熟型的講話速度、方式、語調，我們怎麼可能跟的上？

一旦跟不上，思想中就會產生覺的好難哦、我不行種種念頭，那麼產生了這種潛在式的排斥心理作用，那麼你就更難學的會了。


所以我們必須打好基礎、深厚的基礎，萬丈高樓平地起，唯有打下深厚的基礎，後面你走的路才會越來越順。

程式界裏有句話，程式債欠的越多，最後就只能放棄。學英文也是一樣，英文單字債欠的越多，你就直接上來文法、句型、語調，最後也只能放棄。

基於上述的思維，我打造了這套系統，從最基礎的單字學習起，不斷的強化、加深你對這個單字的聽力，透過各式各樣的影片、各式各樣的人、各種不同的場景。

就只是為了訓練一個單字的聽力，無數的人跟你講同一個單字，講個20遍、50篇、100遍(且不重覆)，我就不信學不起來。


這套系統做出來後，我自己在使用的過程中，確實真的能聽出來他們講的這個單字，而且他們講的速度再快也能聽的出來。

能聽的出來，就知道他講的大概意思；能夠知道他講的大概意思，就能夠用幾個簡單的單字回應；能夠回應，也就能夠對話；當能夠對話，就能夠再修正文法、句型、語調。


我的目地就是希望自己至少能聽的懂人家在說什麼，用英文跟人家對話，大家共同努力、學習。若有其它想法或對這套系統設計的建議，也可以與我聯絡：ichich2013@gmail.com

Eager Execution (TensorFlow Dev Summit 2018)
觀看次數：251
object-oriented
pythonic
way
of
using
tensorflow
that
we're
introducing
to
you
today
as
also
compilers
work
with
data
for
graphs
sub-expression
elimination
like
data
automatic
differentiation
i'm
sure
like
you're
familiar
of
like
other
frameworks
an
a
priori
view
of
your
computations
to
this
computational
graph
you
can
iterate
model
for
debuggers
and
profilers
and
they're
doing
what
they're
doing
and
going
to
run
that
matrix
multiplication
multiplication
for
you
and
give
you
the
computation
you're
executing
computed
based
on
the
computation
and
executing
eagerly
and
we
also
do
gradients
because
i'm
sure
you're
when
you're
not
computing
gradients
iterate
over
your
data
sets
in
datasets
the
python
debugger
anywhere
you
want
and
once
you're
in
the
python
debugger
bottleneck
which
one
is
lower
and
i'm
python
profiler
like
you
would
do
with
collaboratory
thing
which
is
this
interface
for
jupiter
host
book
notebooks
that
are
like
hosted
in
Google
have
a
demo
on
eager
that's
hosted
on
recurring
low
priority
feature
request
is
how
to
customize
gradients
in
tensorflow
stop
gradients
and
functions
and
things
like
that
but
we're
introducing
a
new
as
pythonic
and
object-oriented
as
simpler
a
tensor
for
variable
is
just
a
have
this
like
object-oriented
approach
object-oriented
and
easier
to
use
in
a
metrics
API
so
we're
introducing
this
of
tensile
file
models
if
you've
tried
looking
at
test
flow
checkpoints
now
you
your
checkpoint
so
we're
introducing
a
completely
object-oriented
python
object
variable
that's
reachable
from
your
and
save
and
load
your
discriminators
in
then
you
can
take
a
discriminator
and
for
checkpointing
but
a
real
question
computationally
intensive
you
pretty
we're
as
fast
as
graph
tensorflow
we're
optimizing
iger
performance
very
doing
large
convolutions
large
matrix
multiplications
there
is
almost
no
cost
there's
an
extra
microsecond
of
overhead
if
you're
tracing
gradients
there
are
that
alone
takes
like
single-digit
microseconds
so
if
you
can
execute
enough
computation
to
keep
a
gpu
busy
frameworks
checkpoints
that
I
don't
computation
that
you
really
don't
know
expressing
it
in
using
like
tensile
the
python
function
is
executing
our
convolution
anytime
to
falafel
in
you
can
really
reuse
eager
and
graphical
eager
and
graph
compatibility
should
iterate
debug
and
eager
and
then
import
estimator
deploy
it
on
a
tpu
pod
executing
eagerly
and
when
building
graphs
and
to
do
that
use
the
kara's
layers
they're
great
they're
object
oriented
they're
pythonic
they're
easy
sorts
of
things
automatically
if
you
used
tf
contribs
instead
of
tf
semi
they
learning
or
you're
new
to
tensorflow
and
interactive
later
in
the
session
it's
a
you're
quickly
iterating
over
models
properties
and
you're
comparing
them
and
recursive
orion
ends
there
are
just
much
intuitive
way
of
interacting
with
tensorflow
and
I
hope
you're
going
to
have
a
lot
of
fun
experimenting
with
it
with
collabs
that
I
like
jupiter

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